Trends by population
- removed samples that did not match to a Lab ID from the “FLOW ONLY” tab of “HRS sample list.xlsx”
- removed samples where date could not be parsed
- removed samples where EXPERIMENTER could not be parsed
- removed samples that were manually gated
## [1] "# Start of new population results"
## [1] "naive.Bcells.(CD27-.IgD+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for naive.Bcells.(CD27-.IgD+)"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 47.188, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8234567 0.8611838
## sample estimates:
## cor
## 0.8433564
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 9635500, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9222605
## [1] "naive.Bcells.(CD27-.IgD+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for naive.Bcells.(CD27-.IgD+)"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.65152 -0.10020 0.03273 0.13220 0.35104
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.842e-01 2.905e-01 3.387 0.000709 ***
## DATE_MONTH -2.065e-05 1.687e-05 -1.224 0.221012
## MACHINELSR 2.174e-02 3.998e-03 5.438 5.52e-08 ***
## EXPERIMENTEREC -1.382e-02 8.118e-03 -1.702 0.088708 .
## EXPERIMENTERHB 7.465e-03 6.120e-03 1.220 0.222628
## EXPERIMENTERRR 8.604e-03 6.465e-03 1.331 0.183286
## EXPERIMENTERZF -5.590e-03 4.725e-03 -1.183 0.236839
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1722 on 9072 degrees of freedom
## Multiple R-squared: 0.005491, Adjusted R-squared: 0.004833
## F-statistic: 8.348 on 6 and 9072 DF, p-value: 4.782e-09
##
## [1] "Stepwise Linear Regression for naive.Bcells.(CD27-.IgD+)"
## Start: AIC=-31934.79
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - DATE_MONTH 1 0.04442 269.05 -31935
## <none> 269.01 -31935
## - EXPERIMENTER 4 0.40324 269.41 -31929
## - MACHINE 1 0.87701 269.89 -31907
##
## Step: AIC=-31935.29
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 269.05 -31935
## + DATE_MONTH 1 0.04442 269.01 -31935
## - EXPERIMENTER 4 0.45454 269.51 -31928
## - MACHINE 1 1.06708 270.12 -31901
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 269.0085 -31934.79
## 2 - DATE_MONTH 1 0.04441938 9073 269.0529 -31935.29
## [1] "ANOVA of EXPERIMENTER for naive.Bcells.(CD27-.IgD+)"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.0218613445 -0.0415739033 -0.002148786 0.0209826
## HB-DHS -0.0006895924 -0.0169812166 0.015602032 0.9999599
## RR-DHS 0.0033880691 -0.0131697266 0.019945865 0.9809369
## ZF-DHS -0.0057381948 -0.0185178321 0.007041443 0.7367571
## HB-EC 0.0211717522 -0.0001505602 0.042494065 0.0527032
## RR-EC 0.0252494137 0.0037230441 0.046775783 0.0120229
## ZF-EC 0.0161231498 -0.0026529818 0.034899281 0.1315945
## RR-HB 0.0040776615 -0.0143673008 0.022522624 0.9746358
## ZF-HB -0.0050486024 -0.0201937390 0.010096534 0.8933801
## ZF-RR -0.0091262639 -0.0245573606 0.006304833 0.4886156
##
## [1] "ANOVA of MACHINE for naive.Bcells.(CD27-.IgD+)"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.99 0.9863 33.22 8.5e-09 ***
## Residuals 9077 269.51 0.0297
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "T-test of MACHINE for naive.Bcells.(CD27-.IgD+)"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -5.7357, df = 7174, p-value = 1.011e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02881604 -0.01413616
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.6274223 0.6488984
##
## [1] "# Start of new population results"
## [1] "cytotoxic.Tcells-CD8+"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for cytotoxic.Tcells-CD8+"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 292.74, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9940398 0.9954054
## sample estimates:
## cor
## 0.9947668
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 845340, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9931798
## [1] "cytotoxic.Tcells-CD8+"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for cytotoxic.Tcells-CD8+"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.23815 -0.08556 -0.02092 0.06433 0.66971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.326e-01 1.991e-01 4.181 2.93e-05 ***
## DATE_MONTH -3.412e-05 1.157e-05 -2.950 0.00319 **
## MACHINELSR -3.366e-03 2.740e-03 -1.228 0.21938
## EXPERIMENTEREC 3.655e-03 5.565e-03 0.657 0.51135
## EXPERIMENTERHB 3.525e-03 4.195e-03 0.840 0.40071
## EXPERIMENTERRR 5.824e-03 4.431e-03 1.314 0.18876
## EXPERIMENTERZF 8.566e-03 3.239e-03 2.645 0.00819 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.118 on 9072 degrees of freedom
## Multiple R-squared: 0.001903, Adjusted R-squared: 0.001243
## F-statistic: 2.883 on 6 and 9072 DF, p-value: 0.008288
##
## [1] "Stepwise Linear Regression for cytotoxic.Tcells-CD8+"
## Start: AIC=-38793.32
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - EXPERIMENTER 4 0.103438 126.49 -38794
## - MACHINE 1 0.021017 126.40 -38794
## <none> 126.38 -38793
## - DATE_MONTH 1 0.121241 126.50 -38787
##
## Step: AIC=-38793.89
## TARGET_FREQ ~ DATE_MONTH + MACHINE
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.014264 126.50 -38795
## <none> 126.49 -38794
## + EXPERIMENTER 4 0.103438 126.38 -38793
## - DATE_MONTH 1 0.137313 126.62 -38786
##
## Step: AIC=-38794.87
## TARGET_FREQ ~ DATE_MONTH
##
## Df Sum of Sq RSS AIC
## <none> 126.50 -38795
## + MACHINE 1 0.014264 126.49 -38794
## + EXPERIMENTER 4 0.096685 126.40 -38794
## - DATE_MONTH 1 0.123285 126.62 -38788
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 126.3827 -38793.32
## 2 - EXPERIMENTER 4 0.10343758 9076 126.4861 -38793.89
## 3 - MACHINE 1 0.01426399 9077 126.5004 -38794.87
## [1] "ANOVA of EXPERIMENTER for cytotoxic.Tcells-CD8+"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.003613521 -0.017103810 0.009876769 0.9493784
## HB-DHS 0.002547180 -0.008601993 0.013696353 0.9713926
## RR-DHS 0.001456764 -0.009874563 0.012788092 0.9967606
## ZF-DHS 0.007157432 -0.001588313 0.015903177 0.1676599
## HB-EC 0.006160701 -0.008431223 0.020752626 0.7786893
## RR-EC 0.005070285 -0.009661286 0.019801856 0.8816959
## ZF-EC 0.010770953 -0.002078493 0.023620399 0.1490946
## RR-HB -0.001090416 -0.013713226 0.011532394 0.9993187
## ZF-HB 0.004610252 -0.005754322 0.014974826 0.7435079
## ZF-RR 0.005700668 -0.004859603 0.016260939 0.5802672
##
## [1] "ANOVA of MACHINE for cytotoxic.Tcells-CD8+"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.0 0.000236 0.017 0.897
## Residuals 9077 126.6 0.013950
## [1] "T-test of MACHINE for cytotoxic.Tcells-CD8+"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 0.13007, df = 7295.7, p-value = 0.8965
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.004672884 0.005337083
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.2476015 0.2472694
##
## [1] "# Start of new population results"
## [1] "Tcells.(CD3+.CD19-)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Tcells.(CD3+.CD19-)"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 442.63, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9973810 0.9979818
## sample estimates:
## cor
## 0.9977009
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 403610, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9967437
## [1] "Tcells.(CD3+.CD19-)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for Tcells.(CD3+.CD19-)"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.70008 -0.07236 0.02214 0.09340 0.27335
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.126e-01 2.133e-01 4.280 1.89e-05 ***
## DATE_MONTH -1.284e-05 1.239e-05 -1.037 0.299735
## MACHINELSR 1.979e-02 2.934e-03 6.744 1.64e-11 ***
## EXPERIMENTEREC 3.086e-02 5.959e-03 5.180 2.27e-07 ***
## EXPERIMENTERHB 7.802e-03 4.492e-03 1.737 0.082452 .
## EXPERIMENTERRR -1.055e-02 4.745e-03 -2.223 0.026248 *
## EXPERIMENTERZF -1.187e-02 3.468e-03 -3.423 0.000623 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1264 on 9072 degrees of freedom
## Multiple R-squared: 0.0131, Adjusted R-squared: 0.01245
## F-statistic: 20.08 on 6 and 9072 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for Tcells.(CD3+.CD19-)"
## Start: AIC=-37551.02
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - DATE_MONTH 1 0.01718 144.93 -37552
## <none> 144.91 -37551
## - MACHINE 1 0.72642 145.64 -37508
## - EXPERIMENTER 4 1.29067 146.21 -37479
##
## Step: AIC=-37551.94
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 144.93 -37552
## + DATE_MONTH 1 0.01718 144.91 -37551
## - MACHINE 1 0.85180 145.78 -37501
## - EXPERIMENTER 4 1.28900 146.22 -37480
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 144.9150 -37551.02
## 2 - DATE_MONTH 1 0.01717984 9073 144.9322 -37551.94
## [1] "ANOVA of EXPERIMENTER for Tcells.(CD3+.CD19-)"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0249039186 0.010422220 0.039385617 0.0000271
## HB-DHS 0.0007381132 -0.011230419 0.012706645 0.9998208
## RR-DHS -0.0144696596 -0.026633733 -0.002305586 0.0103475
## ZF-DHS -0.0117773115 -0.021165786 -0.002388837 0.0056278
## HB-EC -0.0241658054 -0.039830099 -0.008501512 0.0002506
## RR-EC -0.0393735781 -0.055187781 -0.023559376 0.0000000
## ZF-EC -0.0366812301 -0.050474989 -0.022887471 0.0000000
## RR-HB -0.0152077728 -0.028758240 -0.001657305 0.0187411
## ZF-HB -0.0125154247 -0.023641697 -0.001389152 0.0183368
## ZF-RR 0.0026923481 -0.008644003 0.014028699 0.9670635
##
## [1] "ANOVA of MACHINE for Tcells.(CD3+.CD19-)"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.62 0.6180 38.36 6.13e-10 ***
## Residuals 9077 146.22 0.0161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "T-test of MACHINE for Tcells.(CD3+.CD19-)"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -6.2219, df = 7393.7, p-value = 5.178e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02235551 -0.01164372
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.6907194 0.7077190
##
## [1] "# Start of new population results"
## [1] "Live.cells.(PE-)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Live.cells.(PE-)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
## [1] "NA for Freq plot"
## [1] "# Start of new population results"
## [1] "IgD-.memory.Bcells.(CD27+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for IgD-.memory.Bcells.(CD27+)"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 51.571, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8463927 0.8795422
## sample estimates:
## cor
## 0.8639
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 11178000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9098132
## [1] "IgD-.memory.Bcells.(CD27+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for IgD-.memory.Bcells.(CD27+)"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13975 -0.06234 -0.02316 0.03335 0.66984
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.529e-01 1.565e-01 -3.533 0.000414 ***
## DATE_MONTH 4.018e-05 9.091e-06 4.420 1e-05 ***
## MACHINELSR -2.666e-02 2.154e-03 -12.379 < 2e-16 ***
## EXPERIMENTEREC 3.548e-03 4.374e-03 0.811 0.417302
## EXPERIMENTERHB -5.014e-03 3.297e-03 -1.521 0.128384
## EXPERIMENTERRR -1.184e-02 3.483e-03 -3.399 0.000680 ***
## EXPERIMENTERZF -5.551e-03 2.546e-03 -2.181 0.029237 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09277 on 9072 degrees of freedom
## Multiple R-squared: 0.0279, Adjusted R-squared: 0.02725
## F-statistic: 43.39 on 6 and 9072 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for IgD-.memory.Bcells.(CD27+)"
## Start: AIC=-43166.31
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 78.074 -43166
## - EXPERIMENTER 4 0.16623 78.240 -43155
## - DATE_MONTH 1 0.16811 78.242 -43149
## - MACHINE 1 1.31869 79.392 -43016
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 78.0738 -43166.31
## [1] "ANOVA of EXPERIMENTER for IgD-.memory.Bcells.(CD27+)"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.016795096 0.006070208 0.0275199845 0.0001896
## HB-DHS 0.005876855 -0.002986827 0.0147405368 0.3683302
## RR-DHS -0.003381406 -0.012389902 0.0056270905 0.8443370
## ZF-DHS -0.004800134 -0.011753072 0.0021528028 0.3262233
## HB-EC -0.010918241 -0.022518938 0.0006824557 0.0764809
## RR-EC -0.020176502 -0.031888219 -0.0084647847 0.0000260
## ZF-EC -0.021595231 -0.031810643 -0.0113798181 0.0000001
## RR-HB -0.009258261 -0.019293496 0.0007769746 0.0869107
## ZF-HB -0.010676989 -0.018916909 -0.0024370695 0.0037512
## ZF-RR -0.001418729 -0.009814229 0.0069767715 0.9907208
##
## [1] "ANOVA of MACHINE for IgD-.memory.Bcells.(CD27+)"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 1.86 1.8645 215.7 <2e-16 ***
## Residuals 9077 78.45 0.0086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "T-test of MACHINE for IgD-.memory.Bcells.(CD27+)"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 14.373, df = 6786.2, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02550054 0.03355476
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.1370574 0.1075298
##
## [1] "# Start of new population results"
## [1] "IgD+.memory.Bcells.(CD27+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for IgD+.memory.Bcells.(CD27+)"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 23.963, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5817408 0.6615343
## sample estimates:
## cor
## 0.6232571
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 13732000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8892079
## [1] "IgD+.memory.Bcells.(CD27+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for IgD+.memory.Bcells.(CD27+)"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14227 -0.07181 -0.02639 0.04242 0.58630
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.346e-01 1.687e-01 -1.391 0.1644
## DATE_MONTH 2.123e-05 9.797e-06 2.167 0.0303 *
## MACHINELSR -6.149e-04 2.321e-03 -0.265 0.7911
## EXPERIMENTEREC 9.085e-03 4.713e-03 1.928 0.0539 .
## EXPERIMENTERHB 2.921e-03 3.553e-03 0.822 0.4110
## EXPERIMENTERRR 1.969e-03 3.753e-03 0.525 0.5998
## EXPERIMENTERZF 3.976e-03 2.743e-03 1.449 0.1473
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09997 on 9072 degrees of freedom
## Multiple R-squared: 0.001869, Adjusted R-squared: 0.001209
## F-statistic: 2.831 on 6 and 9072 DF, p-value: 0.009378
##
## [1] "Stepwise Linear Regression for IgD+.memory.Bcells.(CD27+)"
## Start: AIC=-41808.78
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - EXPERIMENTER 4 0.046168 90.712 -41812
## - MACHINE 1 0.000701 90.666 -41811
## <none> 90.666 -41809
## - DATE_MONTH 1 0.046932 90.713 -41806
##
## Step: AIC=-41812.16
## TARGET_FREQ ~ DATE_MONTH + MACHINE
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.000171 90.712 -41814
## <none> 90.712 -41812
## + EXPERIMENTER 4 0.046168 90.666 -41809
## - DATE_MONTH 1 0.111092 90.823 -41803
##
## Step: AIC=-41814.14
## TARGET_FREQ ~ DATE_MONTH
##
## Df Sum of Sq RSS AIC
## <none> 90.712 -41814
## + MACHINE 1 0.000171 90.712 -41812
## + EXPERIMENTER 4 0.045638 90.666 -41811
## - DATE_MONTH 1 0.123423 90.835 -41804
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 90.66569 -41808.78
## 2 - EXPERIMENTER 4 0.0461682962 9076 90.71185 -41812.16
## 3 - MACHINE 1 0.0001711449 9077 90.71202 -41814.14
## [1] "ANOVA of EXPERIMENTER for IgD+.memory.Bcells.(CD27+)"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0140214684 0.002597490 0.025445447 0.0072861
## HB-DHS 0.0043915476 -0.005049904 0.013832999 0.7103108
## RR-DHS 0.0049798346 -0.004615871 0.014575540 0.6173700
## ZF-DHS 0.0047714632 -0.002634694 0.012177620 0.3986170
## HB-EC -0.0096299208 -0.021986797 0.002726955 0.2089183
## RR-EC -0.0090416338 -0.021516766 0.003433499 0.2771083
## ZF-EC -0.0092500052 -0.020131298 0.001631288 0.1387690
## RR-HB 0.0005882870 -0.010101084 0.011277658 0.9998860
## ZF-HB 0.0003799156 -0.008397114 0.009156945 0.9999562
## ZF-RR -0.0002083714 -0.009151123 0.008734380 0.9999963
##
## [1] "ANOVA of MACHINE for IgD+.memory.Bcells.(CD27+)"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.01 0.01250 1.249 0.264
## Residuals 9077 90.82 0.01001
## [1] "T-test of MACHINE for IgD+.memory.Bcells.(CD27+)"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 1.1264, df = 7467.9, p-value = 0.26
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.001789842 0.006625580
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.1354314 0.1330136
##
## [1] "# Start of new population results"
## [1] "Helper.Tcells-CD4+"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Helper.Tcells-CD4+"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 512.22, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9980425 0.9984917
## sample estimates:
## cor
## 0.9982817
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 194570, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9984302
## [1] "Helper.Tcells-CD4+"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for Helper.Tcells-CD4+"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67778 -0.08244 0.01993 0.09994 0.29133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.811e-02 2.314e-01 0.208 0.83528
## DATE_MONTH 3.649e-05 1.344e-05 2.716 0.00663 **
## MACHINELSR 3.229e-03 3.184e-03 1.014 0.31046
## EXPERIMENTEREC 3.432e-03 6.465e-03 0.531 0.59553
## EXPERIMENTERHB 6.912e-05 4.874e-03 0.014 0.98868
## EXPERIMENTERRR -2.009e-03 5.148e-03 -0.390 0.69641
## EXPERIMENTERZF -7.212e-04 3.763e-03 -0.192 0.84801
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1371 on 9072 degrees of freedom
## Multiple R-squared: 0.001295, Adjusted R-squared: 0.0006345
## F-statistic: 1.961 on 6 and 9072 DF, p-value: 0.06759
##
## [1] "Stepwise Linear Regression for Helper.Tcells-CD4+"
## Start: AIC=-36070.3
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - EXPERIMENTER 4 0.014773 170.60 -36078
## - MACHINE 1 0.019346 170.60 -36071
## <none> 170.59 -36070
## - DATE_MONTH 1 0.138667 170.72 -36065
##
## Step: AIC=-36077.52
## TARGET_FREQ ~ DATE_MONTH + MACHINE
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.020136 170.62 -36078
## <none> 170.60 -36078
## + EXPERIMENTER 4 0.014773 170.59 -36070
## - DATE_MONTH 1 0.206216 170.81 -36069
##
## Step: AIC=-36078.45
## TARGET_FREQ ~ DATE_MONTH
##
## Df Sum of Sq RSS AIC
## <none> 170.62 -36078
## + MACHINE 1 0.020136 170.60 -36078
## + EXPERIMENTER 4 0.015563 170.60 -36071
## - DATE_MONTH 1 0.186281 170.81 -36071
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 170.5860 -36070.30
## 2 - EXPERIMENTER 4 0.01477341 9076 170.6007 -36077.52
## 3 - MACHINE 1 0.02013578 9077 170.6209 -36078.45
## [1] "ANOVA of EXPERIMENTER for Helper.Tcells-CD4+"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0112615108 -0.004410137 0.026933159 0.2855988
## HB-DHS 0.0012330966 -0.011718880 0.014185073 0.9990003
## RR-DHS 0.0027022260 -0.010461359 0.015865811 0.9807076
## ZF-DHS 0.0007738665 -0.009386051 0.010933784 0.9995858
## HB-EC -0.0100284142 -0.026979830 0.006923001 0.4883050
## RR-EC -0.0085592848 -0.025672928 0.008554358 0.6504826
## ZF-EC -0.0104876442 -0.025414825 0.004439536 0.3082727
## RR-HB 0.0014691294 -0.013194769 0.016133028 0.9987782
## ZF-HB -0.0004592301 -0.012499740 0.011581280 0.9999736
## ZF-RR -0.0019283594 -0.014196210 0.010339491 0.9929611
##
## [1] "ANOVA of MACHINE for Helper.Tcells-CD4+"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.0 0.000201 0.011 0.918
## Residuals 9077 170.8 0.018818
## [1] "T-test of MACHINE for Helper.Tcells-CD4+"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -0.10327, df = 7281.3, p-value = 0.9177
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.006122979 0.005510118
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.6785599 0.6788663
##
## [1] "# Start of new population results"
## [1] "B.cells.(CD3-.CD19+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for B.cells.(CD3-.CD19+)"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 95.035, df = 904, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9471045 0.9590023
## sample estimates:
## cor
## 0.9534228
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 8223000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9336565
## [1] "B.cells.(CD3-.CD19+)"
## [1] "n=9079"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 703|
## |EC |FORTESSA | 360|
## |HB |FORTESSA | 848|
## |RR |FORTESSA | 539|
## |ZF |FORTESSA | 998|
## |DHS |LSR | 1520|
## |EC |LSR | 407|
## |HB |LSR | 489|
## |RR |LSR | 731|
## |ZF |LSR | 2484|
## [1] "Linear Regression for B.cells.(CD3-.CD19+)"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.08970 -0.03071 -0.01149 0.01633 0.87414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.961e-01 8.861e-02 -10.113 < 2e-16 ***
## DATE_MONTH 5.575e-05 5.146e-06 10.833 < 2e-16 ***
## MACHINELSR -5.079e-04 1.219e-03 -0.417 0.676985
## EXPERIMENTEREC -1.211e-02 2.476e-03 -4.892 1.02e-06 ***
## EXPERIMENTERHB 6.224e-03 1.866e-03 3.335 0.000857 ***
## EXPERIMENTERRR 5.919e-03 1.972e-03 3.002 0.002689 **
## EXPERIMENTERZF 1.694e-02 1.441e-03 11.757 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.05251 on 9072 degrees of freedom
## Multiple R-squared: 0.03641, Adjusted R-squared: 0.03577
## F-statistic: 57.13 on 6 and 9072 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for B.cells.(CD3-.CD19+)"
## Start: AIC=-53498.7
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.00048 25.019 -53501
## <none> 25.018 -53499
## - DATE_MONTH 1 0.32365 25.342 -53384
## - EXPERIMENTER 4 0.69644 25.715 -53257
##
## Step: AIC=-53500.53
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 25.019 -53501
## + MACHINE 1 0.00048 25.018 -53499
## - DATE_MONTH 1 0.35696 25.376 -53374
## - EXPERIMENTER 4 0.69602 25.715 -53259
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 9072 25.01814 -53498.70
## 2 - MACHINE 1 0.0004786054 9073 25.01862 -53500.53
## [1] "ANOVA of EXPERIMENTER for B.cells.(CD3-.CD19+)"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0006834458 -0.0053584415 0.006725333 0.9980333
## HB-DHS 0.0097330087 0.0047396357 0.014726382 0.0000011
## RR-DHS 0.0137048793 0.0086299249 0.018779834 0.0000000
## ZF-DHS 0.0190650663 0.0151481151 0.022982017 0.0000000
## HB-EC 0.0090495629 0.0025142870 0.015584839 0.0014953
## RR-EC 0.0130214335 0.0064236142 0.019619253 0.0000007
## ZF-EC 0.0183816204 0.0126267474 0.024136493 0.0000000
## RR-HB 0.0039718706 -0.0016814993 0.009625240 0.3083019
## ZF-HB 0.0093320575 0.0046900823 0.013974033 0.0000004
## ZF-RR 0.0053601870 0.0006305652 0.010089809 0.0170475
##
## [1] "ANOVA of MACHINE for B.cells.(CD3-.CD19+)"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.018 0.017863 6.249 0.0124 *
## Residuals 9077 25.946 0.002858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "T-test of MACHINE for B.cells.(CD3-.CD19+)"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 2.5052, df = 7337.6, p-value = 0.01226
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.0006286479 0.0051516720
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.07341851 0.07052835
##
## [1] "# Start of new population results"
## [1] "Non.classical.monocytes"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Non.classical.monocytes"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 3.8727, df = 763, p-value = 0.0001168
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.06863707 0.20768346
## sample estimates:
## cor
## 0.1388445
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 31786000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5739997
## [1] "Non.classical.monocytes"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for Non.classical.monocytes"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04761 -0.02109 -0.01023 0.00709 0.82466
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.718e-01 7.205e-02 5.160 2.53e-07 ***
## DATE_MONTH -1.943e-05 4.184e-06 -4.643 3.49e-06 ***
## MACHINELSR -8.881e-05 9.761e-04 -0.091 0.928
## EXPERIMENTEREC -1.336e-03 1.987e-03 -0.673 0.501
## EXPERIMENTERHB 1.120e-04 1.516e-03 0.074 0.941
## EXPERIMENTERRR 6.327e-03 1.561e-03 4.053 5.10e-05 ***
## EXPERIMENTERZF 5.878e-03 1.160e-03 5.069 4.09e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0405 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009652, Adjusted R-squared: 0.008942
## F-statistic: 13.6 on 6 and 8371 DF, p-value: 2.009e-15
##
## [1] "Stepwise Linear Regression for Non.classical.monocytes"
## Start: AIC=-53718.84
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.000014 13.733 -53721
## <none> 13.733 -53719
## - DATE_MONTH 1 0.035366 13.768 -53699
## - EXPERIMENTER 4 0.080576 13.814 -53678
##
## Step: AIC=-53720.83
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 13.733 -53721
## + MACHINE 1 0.000014 13.733 -53719
## - DATE_MONTH 1 0.037639 13.771 -53700
## - EXPERIMENTER 4 0.081473 13.815 -53679
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 13.73298 -53718.84
## 2 - MACHINE 1 1.358177e-05 8372 13.73300 -53720.83
## [1] "ANOVA of EXPERIMENTER for Non.classical.monocytes"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.005680981 -0.0105028641 -0.0008590979 0.0114727
## HB-DHS -0.000994998 -0.0050371111 0.0030471151 0.9625189
## RR-DHS 0.003744247 -0.0002569387 0.0077454336 0.0793908
## ZF-DHS 0.005145884 0.0020140858 0.0082776814 0.0000730
## HB-EC 0.004685983 -0.0005651840 0.0099371501 0.1062456
## RR-EC 0.009425228 0.0042054998 0.0146449571 0.0000084
## ZF-EC 0.010826865 0.0062394651 0.0154142641 0.0000000
## RR-HB 0.004739245 0.0002299562 0.0092485347 0.0337404
## ZF-HB 0.006140882 0.0023815790 0.0099001842 0.0000824
## ZF-RR 0.001401636 -0.0023136253 0.0051168977 0.8418699
##
## [1] "ANOVA of MACHINE for Non.classical.monocytes"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.008 0.008443 5.103 0.0239 *
## Residuals 8376 13.858 0.001655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for Non.classical.monocytes"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -2.3469, df = 7496, p-value = 0.01896
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0037985081 -0.0003409556
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.03862871 0.04069844
##
## [1] "# Start of new population results"
## [1] "Myeloid.DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Myeloid.DC"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 40.064, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7990358 0.8448681
## sample estimates:
## cor
## 0.8232893
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 9054000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8786583
## [1] "Myeloid.DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for Myeloid.DC"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.66987 -0.05656 0.03294 0.09387 0.25678
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.071e+00 2.421e-01 8.554 < 2e-16 ***
## DATE_MONTH -7.822e-05 1.406e-05 -5.563 2.74e-08 ***
## MACHINELSR -3.276e-02 3.280e-03 -9.988 < 2e-16 ***
## EXPERIMENTEREC -2.762e-02 6.676e-03 -4.136 3.56e-05 ***
## EXPERIMENTERHB -1.891e-02 5.093e-03 -3.714 0.000206 ***
## EXPERIMENTERRR -2.531e-02 5.246e-03 -4.824 1.43e-06 ***
## EXPERIMENTERZF -2.187e-02 3.897e-03 -5.611 2.07e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1361 on 8370 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.02208, Adjusted R-squared: 0.02138
## F-statistic: 31.49 on 6 and 8370 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for Myeloid.DC"
## Start: AIC=-33409.55
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 154.99 -33410
## - DATE_MONTH 1 0.57295 155.56 -33381
## - EXPERIMENTER 4 0.73020 155.72 -33378
## - MACHINE 1 1.84735 156.83 -33312
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8370 154.9847 -33409.55
## [1] "ANOVA of EXPERIMENTER for Myeloid.DC"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.040695085 -0.056978480 -2.441169e-02 0.0000000
## HB-DHS -0.013602948 -0.027253443 4.754718e-05 0.0513184
## RR-DHS -0.032332549 -0.045844856 -1.882024e-02 0.0000000
## ZF-DHS -0.026331294 -0.036908226 -1.575436e-02 0.0000000
## HB-EC 0.027092137 0.009360184 4.482409e-02 0.0002990
## RR-EC 0.008362536 -0.009263258 2.598833e-02 0.6946678
## ZF-EC 0.014363791 -0.001126776 2.985436e-02 0.0841795
## RR-HB -0.018729601 -0.033956408 -3.502795e-03 0.0071056
## ZF-HB -0.012728346 -0.025422625 -3.406731e-05 0.0490037
## ZF-RR 0.006001255 -0.006544307 1.854682e-02 0.6880409
##
## [1] "ANOVA of MACHINE for Myeloid.DC"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 1.34 1.3435 71.6 <2e-16 ***
## Residuals 8375 157.14 0.0188
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
## [1] "T-test of MACHINE for Myeloid.DC"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 8.6804, df = 7237.2, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02021393 0.03200697
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.7015455 0.6754350
##
## [1] "# Start of new population results"
## [1] "DC.NK"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for DC.NK"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 105.48, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9624955 0.9716366
## sample estimates:
## cor
## 0.9673794
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 3068600, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9588748
## [1] "DC.NK"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for DC.NK"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51911 -0.11195 0.00576 0.11958 0.45709
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.789e+00 2.847e-01 6.285 3.45e-10 ***
## DATE_MONTH -7.172e-05 1.653e-05 -4.338 1.46e-05 ***
## MACHINELSR -2.881e-02 3.857e-03 -7.468 8.93e-14 ***
## EXPERIMENTEREC 8.022e-02 7.851e-03 10.217 < 2e-16 ***
## EXPERIMENTERHB 3.592e-02 5.990e-03 5.997 2.09e-09 ***
## EXPERIMENTERRR 4.400e-02 6.169e-03 7.133 1.06e-12 ***
## EXPERIMENTERZF 7.028e-03 4.582e-03 1.534 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1601 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.02636, Adjusted R-squared: 0.02566
## F-statistic: 37.77 on 6 and 8371 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for DC.NK"
## Start: AIC=-30694.26
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 214.44 -30694
## - DATE_MONTH 1 0.4820 214.92 -30677
## - MACHINE 1 1.4288 215.87 -30641
## - EXPERIMENTER 4 3.7657 218.20 -30556
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 214.4379 -30694.26
## [1] "ANOVA of EXPERIMENTER for DC.NK"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.068066709 0.048968752 0.087164666 0.0000000
## HB-DHS 0.040432130 0.024422596 0.056441664 0.0000000
## RR-DHS 0.037446395 0.021598960 0.053293830 0.0000000
## ZF-DHS 0.003002906 -0.009401156 0.015406968 0.9647005
## HB-EC -0.027634579 -0.048432794 -0.006836364 0.0026888
## RR-EC -0.030620314 -0.051294011 -0.009946617 0.0005147
## ZF-EC -0.065063803 -0.083233044 -0.046894562 0.0000000
## RR-HB -0.002985735 -0.020845606 0.014874136 0.9910930
## ZF-HB -0.037429224 -0.052318635 -0.022539813 0.0000000
## ZF-RR -0.034443489 -0.049158467 -0.019728511 0.0000000
##
## [1] "ANOVA of MACHINE for DC.NK"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 1.99 1.9949 76.56 <2e-16 ***
## Residuals 8376 218.25 0.0261
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for DC.NK"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 8.5551, df = 6218.9, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.02452516 0.03910566
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.5759788 0.5441634
##
## [1] "# Start of new population results"
## [1] "MONOCYTES"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for MONOCYTES"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 58.747, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8912443 0.9170173
## sample estimates:
## cor
## 0.9049574
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 6793600, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9089519
## [1] "MONOCYTES"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for MONOCYTES"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.30179 -0.10191 -0.01327 0.08614 0.70205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.115e-01 2.418e-01 3.769 0.000165 ***
## DATE_MONTH -3.530e-05 1.405e-05 -2.514 0.011972 *
## MACHINELSR 8.167e-03 3.276e-03 2.493 0.012699 *
## EXPERIMENTEREC -6.508e-02 6.669e-03 -9.759 < 2e-16 ***
## EXPERIMENTERHB -4.750e-02 5.088e-03 -9.335 < 2e-16 ***
## EXPERIMENTERRR -4.870e-02 5.240e-03 -9.294 < 2e-16 ***
## EXPERIMENTERZF -1.375e-02 3.892e-03 -3.533 0.000413 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.136 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.03442, Adjusted R-squared: 0.03373
## F-statistic: 49.74 on 6 and 8371 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for MONOCYTES"
## Start: AIC=-33428.4
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 154.73 -33428
## - MACHINE 1 0.1148 154.84 -33424
## - DATE_MONTH 1 0.1168 154.84 -33424
## - EXPERIMENTER 4 3.2554 157.98 -33262
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 154.7285 -33428.4
## [1] "ANOVA of EXPERIMENTER for MONOCYTES"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.074117076 -0.090296632 -0.057937521 0.0000000
## HB-DHS -0.052024087 -0.065587169 -0.038461006 0.0000000
## RR-DHS -0.054266535 -0.067692288 -0.040840782 0.0000000
## ZF-DHS -0.014696709 -0.025205279 -0.004188139 0.0012893
## HB-EC 0.022092989 0.004472996 0.039712982 0.0056604
## RR-EC 0.019850541 0.002336038 0.037365045 0.0170403
## ZF-EC 0.059420368 0.044027609 0.074813126 0.0000000
## RR-HB -0.002242448 -0.017373112 0.012888216 0.9943843
## ZF-HB 0.037327378 0.024713252 0.049941505 0.0000000
## ZF-RR 0.039569826 0.027103477 0.052036176 0.0000000
##
## [1] "ANOVA of MACHINE for MONOCYTES"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.73 0.7295 38.31 6.33e-10 ***
## Residuals 8376 159.52 0.0190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for MONOCYTES"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -6.1345, df = 6505.2, p-value = 9.047e-10
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02538778 -0.01309137
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.2714479 0.2906874
##
## [1] "# Start of new population results"
## [1] "NK"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for NK"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 181.83, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9869381 0.9901516
## sample estimates:
## cor
## 0.9886575
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 1214000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9837303
## [1] "NK"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for NK"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.61133 -0.09399 0.01928 0.11253 0.33694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.3648499 0.2703692 8.747 < 2e-16 ***
## DATE_MONTH -0.0001021 0.0000157 -6.501 8.46e-11 ***
## MACHINELSR -0.0098266 0.0036629 -2.683 0.00732 **
## EXPERIMENTEREC 0.0444268 0.0074557 5.959 2.64e-09 ***
## EXPERIMENTERHB 0.0134297 0.0056882 2.361 0.01825 *
## EXPERIMENTERRR 0.0061400 0.0058583 1.048 0.29463
## EXPERIMENTERZF 0.0071803 0.0043515 1.650 0.09896 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.152 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.007545, Adjusted R-squared: 0.006834
## F-statistic: 10.61 on 6 and 8371 DF, p-value: 9.078e-12
##
## [1] "Stepwise Linear Regression for NK"
## Start: AIC=-31560.33
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 193.38 -31560
## - MACHINE 1 0.16626 193.54 -31555
## - EXPERIMENTER 4 0.90744 194.28 -31529
## - DATE_MONTH 1 0.97620 194.35 -31520
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 193.3778 -31560.33
## [1] "ANOVA of EXPERIMENTER for NK"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.022879513 0.004763522 4.099550e-02 0.0051837
## HB-DHS 0.010439305 -0.004747061 2.562567e-02 0.3306480
## RR-DHS -0.006452284 -0.021484887 8.580318e-03 0.7679713
## ZF-DHS 0.002899061 -0.008867217 1.466534e-02 0.9623933
## HB-EC -0.012440208 -0.032169034 7.288618e-03 0.4213301
## RR-EC -0.029331797 -0.048942508 -9.721086e-03 0.0004353
## ZF-EC -0.019980452 -0.037215479 -2.745424e-03 0.0135817
## RR-HB -0.016891589 -0.033833154 4.997466e-05 0.0511147
## ZF-HB -0.007540244 -0.021664081 6.583593e-03 0.5908734
## ZF-RR 0.009351346 -0.004607027 2.330972e-02 0.3574285
##
## [1] "ANOVA of MACHINE for NK"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.05 0.04738 2.037 0.154
## Residuals 8376 194.80 0.02326
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for NK"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 1.4163, df = 6530.7, p-value = 0.1567
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.001883226 0.011689026
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.6117531 0.6068502
##
## [1] "# Start of new population results"
## [1] "DC.NK.MONOCYTES"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for DC.NK.MONOCYTES"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 111.8, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9664312 0.9746254
## sample estimates:
## cor
## 0.9708103
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 2393700, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9679193
## [1] "DC.NK.MONOCYTES"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for DC.NK.MONOCYTES"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.28125 -0.09001 -0.02334 0.06853 0.65596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -5.494e-01 2.187e-01 -2.512 0.012016 *
## DATE_MONTH 4.822e-05 1.270e-05 3.797 0.000148 ***
## MACHINELSR 8.936e-03 2.962e-03 3.017 0.002564 **
## EXPERIMENTEREC -5.926e-02 6.030e-03 -9.827 < 2e-16 ***
## EXPERIMENTERHB -2.909e-02 4.600e-03 -6.323 2.7e-10 ***
## EXPERIMENTERRR -2.775e-02 4.738e-03 -5.857 4.9e-09 ***
## EXPERIMENTERZF -1.187e-02 3.519e-03 -3.374 0.000744 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1229 on 8372 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01543, Adjusted R-squared: 0.01472
## F-statistic: 21.86 on 6 and 8372 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for DC.NK.MONOCYTES"
## Start: AIC=-35119.16
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 126.53 -35119
## - MACHINE 1 0.13753 126.67 -35112
## - DATE_MONTH 1 0.21787 126.75 -35107
## - EXPERIMENTER 4 1.73996 128.27 -35013
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8372 126.5304 -35119.16
## [1] "ANOVA of EXPERIMENTER for DC.NK.MONOCYTES"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.049660576 -0.064291434 -0.0350297178 0.0000000
## HB-DHS -0.028969180 -0.041233683 -0.0167046771 0.0000000
## RR-DHS -0.022247474 -0.034387774 -0.0101071737 0.0000058
## ZF-DHS -0.009650417 -0.019152288 -0.0001485466 0.0444285
## HB-EC 0.020691396 0.004756968 0.0366258238 0.0036494
## RR-EC 0.027413102 0.011574072 0.0432521313 0.0000234
## ZF-EC 0.040010159 0.026089903 0.0539304140 0.0000000
## RR-HB 0.006721706 -0.006961527 0.0204049391 0.6659470
## ZF-HB 0.019318763 0.007911330 0.0307261956 0.0000382
## ZF-RR 0.012597057 0.001323265 0.0238708490 0.0195470
##
## [1] "ANOVA of MACHINE for DC.NK.MONOCYTES"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.22 0.22365 14.6 0.000134 ***
## Residuals 8377 128.29 0.01531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness
## [1] "T-test of MACHINE for DC.NK.MONOCYTES"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -3.7648, df = 6379.5, p-value = 0.0001682
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.016197914 -0.005105314
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.2629568 0.2736084
##
## [1] "# Start of new population results"
## [1] "NK.CD56HI"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for NK.CD56HI"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 13.233, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3725683 0.4879804
## sample estimates:
## cor
## 0.4320415
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 47262000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3665909
## [1] "NK.CD56HI"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for NK.CD56HI"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.01000 -0.00620 -0.00357 0.00111 0.35762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.688e-02 2.645e-02 2.151 0.03151 *
## DATE_MONTH -2.764e-06 1.536e-06 -1.799 0.07198 .
## MACHINELSR -1.099e-04 3.582e-04 -0.307 0.75898
## EXPERIMENTEREC -6.274e-04 7.293e-04 -0.860 0.38963
## EXPERIMENTERHB -1.021e-03 5.563e-04 -1.836 0.06638 .
## EXPERIMENTERRR -3.391e-05 5.732e-04 -0.059 0.95283
## EXPERIMENTERZF -1.145e-03 4.256e-04 -2.691 0.00714 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01486 on 8367 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.001714, Adjusted R-squared: 0.0009982
## F-statistic: 2.394 on 6 and 8367 DF, p-value: 0.02589
##
## [1] "Stepwise Linear Regression for NK.CD56HI"
## Start: AIC=-70488.23
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.00002078 1.8473 -70490
## <none> 1.8473 -70488
## - DATE_MONTH 1 0.00071492 1.8480 -70487
## - EXPERIMENTER 4 0.00227598 1.8495 -70486
##
## Step: AIC=-70490.14
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 1.8473 -70490
## - DATE_MONTH 1 0.00070093 1.8480 -70489
## + MACHINE 1 0.00002078 1.8473 -70488
## - EXPERIMENTER 4 0.00227781 1.8496 -70488
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8367 1.847263 -70488.23
## 2 - MACHINE 1 2.07846e-05 8368 1.847284 -70490.14
## [1] "ANOVA of EXPERIMENTER for NK.CD56HI"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -1.232961e-03 -0.0030001187 0.0005341973 0.3153674
## HB-DHS -1.150332e-03 -0.0026318332 0.0003311701 0.2121730
## RR-DHS -3.920065e-04 -0.0018588902 0.0010748773 0.9497886
## ZF-DHS -1.254730e-03 -0.0024027929 -0.0001066670 0.0240089
## HB-EC 8.262912e-05 -0.0018414895 0.0020067477 0.9999576
## RR-EC 8.409542e-04 -0.0010719319 0.0027538403 0.7517350
## ZF-EC -2.176924e-05 -0.0017026719 0.0016591334 0.9999996
## RR-HB 7.583251e-04 -0.0008942889 0.0024109391 0.7206810
## ZF-HB -1.043984e-04 -0.0014818720 0.0012730753 0.9995938
## ZF-RR -8.627235e-04 -0.0022244629 0.0004990160 0.4163351
##
## [1] "ANOVA of MACHINE for NK.CD56HI"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.00 1.254e-05 0.057 0.812
## Residuals 8372 1.85 2.210e-04
## 7 observations deleted due to missingness
## [1] "T-test of MACHINE for NK.CD56HI"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -0.23135, df = 6073.6, p-value = 0.8171
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0007560212 0.0005964143
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.008482688 0.008562492
##
## [1] "# Start of new population results"
## [1] "NK.CD56LO"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for NK.CD56LO"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 183.14, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9871208 0.9902896
## sample estimates:
## cor
## 0.9888162
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 1165800, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9843763
## [1] "NK.CD56LO"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for NK.CD56LO"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.92853 -0.01268 0.01870 0.03667 0.07116
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.270e+00 1.198e-01 10.596 < 2e-16 ***
## DATE_MONTH -1.987e-05 6.958e-06 -2.855 0.00431 **
## MACHINELSR 6.785e-03 1.623e-03 4.181 2.93e-05 ***
## EXPERIMENTEREC 1.754e-02 3.304e-03 5.309 1.13e-07 ***
## EXPERIMENTERHB 1.417e-03 2.520e-03 0.562 0.57391
## EXPERIMENTERRR 3.634e-03 2.597e-03 1.399 0.16177
## EXPERIMENTERZF 4.005e-03 1.928e-03 2.077 0.03783 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06732 on 8367 degrees of freedom
## (7 observations deleted due to missingness)
## Multiple R-squared: 0.006951, Adjusted R-squared: 0.006239
## F-statistic: 9.76 on 6 and 8367 DF, p-value: 9.628e-11
##
## [1] "Stepwise Linear Regression for NK.CD56LO"
## Start: AIC=-45184.87
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 37.916 -45185
## - DATE_MONTH 1 0.036943 37.953 -45179
## - MACHINE 1 0.079229 37.995 -45169
## - EXPERIMENTER 4 0.137925 38.054 -45162
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8367 37.91559 -45184.87
## [1] "ANOVA of EXPERIMENTER for NK.CD56LO"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0121506378 0.004129704 0.0201715716 0.0003484
## HB-DHS -0.0018000974 -0.008524469 0.0049242740 0.9494835
## RR-DHS 0.0002668863 -0.006391136 0.0069249086 0.9999678
## ZF-DHS 0.0035665834 -0.001644347 0.0087775135 0.3351272
## HB-EC -0.0139507352 -0.022684096 -0.0052173745 0.0001293
## RR-EC -0.0118837515 -0.020566129 -0.0032013740 0.0017730
## ZF-EC -0.0085840544 -0.016213485 -0.0009546238 0.0182952
## RR-HB 0.0020669837 -0.005434048 0.0095680150 0.9440844
## ZF-HB 0.0053666808 -0.000885519 0.0116188806 0.1318665
## ZF-RR 0.0032996971 -0.002881087 0.0094804812 0.5908781
##
## [1] "ANOVA of MACHINE for NK.CD56LO"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.13 0.12565 27.64 1.5e-07 ***
## Residuals 8372 38.06 0.00455
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 7 observations deleted due to missingness
## [1] "T-test of MACHINE for NK.CD56LO"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -5.0157, df = 5719.6, p-value = 5.442e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.011109125 -0.004865514
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.9301165 0.9381039
##
## [1] "# Start of new population results"
## [1] "Plasmacytoid.DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Plasmacytoid.DC"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 2.2502, df = 763, p-value = 0.02472
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.01037005 0.15120601
## sample estimates:
## cor
## 0.0811933
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 26078000, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.6505109
## [1] "Plasmacytoid.DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for Plasmacytoid.DC"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.18320 -0.08208 -0.03052 0.04394 0.73823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.362e-03 2.115e-01 -0.030 0.97601
## DATE_MONTH 9.928e-06 1.228e-05 0.808 0.41897
## MACHINELSR 1.299e-02 2.865e-03 4.536 5.82e-06 ***
## EXPERIMENTEREC 2.789e-02 5.832e-03 4.782 1.77e-06 ***
## EXPERIMENTERHB 1.215e-02 4.449e-03 2.730 0.00634 **
## EXPERIMENTERRR 1.428e-02 4.583e-03 3.116 0.00184 **
## EXPERIMENTERZF 1.337e-02 3.404e-03 3.928 8.64e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1189 on 8370 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.006779, Adjusted R-squared: 0.006067
## F-statistic: 9.522 on 6 and 8370 DF, p-value: 1.87e-10
##
## [1] "Stepwise Linear Regression for Plasmacytoid.DC"
## Start: AIC=-35674.33
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - DATE_MONTH 1 0.00923 118.28 -35676
## <none> 118.27 -35674
## - MACHINE 1 0.29069 118.56 -35656
## - EXPERIMENTER 4 0.39249 118.66 -35655
##
## Step: AIC=-35675.68
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 118.28 -35676
## + DATE_MONTH 1 0.00923 118.27 -35674
## - MACHINE 1 0.28382 118.56 -35658
## - EXPERIMENTER 4 0.54746 118.83 -35645
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8370 118.2698 -35674.33
## 2 - DATE_MONTH 1 0.009230744 8371 118.2791 -35675.68
## [1] "ANOVA of EXPERIMENTER for Plasmacytoid.DC"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0283415199 0.014191157 0.0424918826 0.0000005
## HB-DHS 0.0088073102 -0.003055047 0.0206696677 0.2536501
## RR-DHS 0.0142483872 0.002506116 0.0259906584 0.0083040
## ZF-DHS 0.0143493114 0.005157897 0.0235407257 0.0002010
## HB-EC -0.0195342097 -0.034943378 -0.0041250418 0.0049492
## RR-EC -0.0140931327 -0.029410047 0.0012237815 0.0883780
## ZF-EC -0.0139922085 -0.027453599 -0.0005308183 0.0369619
## RR-HB 0.0054410770 -0.007791104 0.0186732583 0.7950292
## ZF-HB 0.0055420012 -0.005489399 0.0165734013 0.6465541
## ZF-RR 0.0001009242 -0.010801241 0.0110030890 0.9999999
##
## [1] "ANOVA of MACHINE for Plasmacytoid.DC"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.25 0.25059 17.66 2.67e-05 ***
## Residuals 8375 118.83 0.01419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
## [1] "T-test of MACHINE for Plasmacytoid.DC"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = -4.3068, df = 7216.6, p-value = 1.678e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.01640910 -0.00614389
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.1771431 0.1884196
##
## [1] "# Start of new population results"
## [1] "Classical.monocytes"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for Classical.monocytes"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 64.557, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9076420 0.9296736
## sample estimates:
## cor
## 0.9193757
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 5696900, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.9236505
## [1] "Classical.monocytes"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for Classical.monocytes"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.82467 -0.00700 0.01042 0.02134 0.04698
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.392e-01 7.347e-02 8.700 < 2e-16 ***
## DATE_MONTH 1.875e-05 4.266e-06 4.395 1.12e-05 ***
## MACHINELSR 3.168e-04 9.953e-04 0.318 0.750
## EXPERIMENTEREC 1.633e-03 2.026e-03 0.806 0.420
## EXPERIMENTERHB -4.822e-04 1.546e-03 -0.312 0.755
## EXPERIMENTERRR -7.053e-03 1.592e-03 -4.431 9.52e-06 ***
## EXPERIMENTERZF -5.987e-03 1.182e-03 -5.064 4.20e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0413 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.009403, Adjusted R-squared: 0.008693
## F-statistic: 13.24 on 6 and 8371 DF, p-value: 5.481e-15
##
## [1] "Stepwise Linear Regression for Classical.monocytes"
## Start: AIC=-53392.91
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## - MACHINE 1 0.000173 14.278 -53395
## <none> 14.278 -53393
## - DATE_MONTH 1 0.032950 14.311 -53376
## - EXPERIMENTER 4 0.088336 14.366 -53349
##
## Step: AIC=-53394.81
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 14.278 -53395
## + MACHINE 1 0.000173 14.278 -53393
## - DATE_MONTH 1 0.034088 14.312 -53377
## - EXPERIMENTER 4 0.088505 14.366 -53351
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 14.27776 -53392.91
## 2 - MACHINE 1 0.0001728299 8372 14.27793 -53394.81
## [1] "ANOVA of EXPERIMENTER for Classical.monocytes"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS 0.0057947587 0.0008790045 1.071051e-02 0.0114059
## HB-DHS 0.0005166232 -0.0036041807 4.637427e-03 0.9970620
## RR-DHS -0.0045835304 -0.0086626106 -5.044502e-04 0.0185249
## ZF-DHS -0.0052699299 -0.0084626966 -2.077163e-03 0.0000664
## HB-EC -0.0052781355 -0.0106315309 7.525984e-05 0.0555014
## RR-EC -0.0103782891 -0.0156996341 -5.056944e-03 0.0000011
## ZF-EC -0.0110646886 -0.0157413943 -6.387983e-03 0.0000000
## RR-HB -0.0051001536 -0.0096972285 -5.030787e-04 0.0209077
## ZF-HB -0.0057865531 -0.0096190408 -1.954065e-03 0.0003693
## ZF-RR -0.0006863995 -0.0044739887 3.101190e-03 0.9879043
##
## [1] "ANOVA of MACHINE for Classical.monocytes"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.006 0.005968 3.47 0.0625 .
## Residuals 8376 14.407 0.001720
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for Classical.monocytes"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 1.9376, df = 7519.5, p-value = 0.05272
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2.041205e-05 3.500760e-03
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.9604032 0.9586630
##
## [1] "# Start of new population results"
## [1] "Live.immune.cells"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Live.immune.cells"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
## [1] "NA for Freq plot"
## [1] "# Start of new population results"
## [1] "Live.Single.PBMCs"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Live.Single.PBMCs"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
## [1] "NA for Freq plot"
## [1] "# Start of new population results"
## [1] "DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time COUNT OC"
## [1] "Correlations between OC and manual for DC"
##
## Pearson's product-moment correlation
##
## data: merge[, colC] and merge[, coljflowCount]
## t = 18.851, df = 763, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5133265 0.6102001
## sample estimates:
## cor
## 0.5636987
##
##
## Spearman's rank correlation rho
##
## data: merge[, colC] and merge[, coljflowCount]
## S = 8532500, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8856482
## [1] "DC"
## [1] "n=8381"
## [1] "PLOT TYPE = Machine Time FREQ"
##
##
## |Var1 |Var2 | Freq|
## |:----|:--------|----:|
## |DHS |FORTESSA | 668|
## |EC |FORTESSA | 329|
## |HB |FORTESSA | 745|
## |RR |FORTESSA | 528|
## |ZF |FORTESSA | 903|
## |DHS |LSR | 1372|
## |EC |LSR | 381|
## |HB |LSR | 440|
## |RR |LSR | 697|
## |ZF |LSR | 2318|
## [1] "Linear Regression for DC"
##
## Call:
## lm(formula = formula, data = subF)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.17463 -0.06427 -0.02041 0.04169 0.84764
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.439e-01 1.675e-01 -1.456 0.1454
## DATE_MONTH 2.461e-05 9.728e-06 2.530 0.0114 *
## MACHINELSR -9.139e-03 2.269e-03 -4.027 5.69e-05 ***
## EXPERIMENTEREC -5.455e-02 4.619e-03 -11.810 < 2e-16 ***
## EXPERIMENTERHB -2.020e-02 3.524e-03 -5.731 1.03e-08 ***
## EXPERIMENTERRR -3.251e-02 3.629e-03 -8.958 < 2e-16 ***
## EXPERIMENTERZF -2.584e-02 2.696e-03 -9.583 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09416 on 8371 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.02399, Adjusted R-squared: 0.02329
## F-statistic: 34.3 on 6 and 8371 DF, p-value: < 2.2e-16
##
## [1] "Stepwise Linear Regression for DC"
## Start: AIC=-39582.57
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Df Sum of Sq RSS AIC
## <none> 74.226 -39583
## - DATE_MONTH 1 0.05675 74.282 -39578
## - MACHINE 1 0.14382 74.369 -39568
## - EXPERIMENTER 4 1.53711 75.763 -39419
## Stepwise Model Path
## Analysis of Deviance Table
##
## Initial Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
## Final Model:
## TARGET_FREQ ~ DATE_MONTH + MACHINE + EXPERIMENTER
##
##
## Step Df Deviance Resid. Df Resid. Dev AIC
## 1 8371 74.22561 -39582.57
## [1] "ANOVA of EXPERIMENTER for DC"
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = formula, data = subF)
##
## $EXPERIMENTER
## diff lwr upr p adj
## EC-DHS -0.047785470 -0.059000403 -0.0365705381 0.0000000
## HB-DHS -0.016000733 -0.025402045 -0.0065994213 0.0000342
## RR-DHS -0.028272837 -0.037578958 -0.0189667146 0.0000000
## ZF-DHS -0.025337948 -0.032622011 -0.0180538851 0.0000000
## HB-EC 0.031784738 0.019571359 0.0439981160 0.0000000
## RR-EC 0.019512634 0.007372376 0.0316528918 0.0001144
## ZF-EC 0.022447522 0.011777962 0.0331170832 0.0000001
## RR-HB -0.012272104 -0.022759992 -0.0017842148 0.0123329
## ZF-HB -0.009337215 -0.018080755 -0.0005936758 0.0294680
## ZF-RR 0.002934888 -0.005706218 0.0115759952 0.8866651
##
## [1] "ANOVA of MACHINE for DC"
## Df Sum Sq Mean Sq F value Pr(>F)
## MACHINE 1 0.17 0.16979 18.74 1.51e-05 ***
## Residuals 8376 75.88 0.00906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 3 observations deleted due to missingness
## [1] "T-test of MACHINE for DC"
##
## Welch Two Sample t-test
##
## data: TARGET_FREQ by MACHINE
## t = 4.1995, df = 6056.3, p-value = 2.713e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.004948977 0.013614586
## sample estimates:
## mean in group FORTESSA mean in group LSR
## 0.1581965 0.1489147